What happened to my 2018 AR & AI predictions and what will happen next?

Screen Shot 2018-12-27 at 5.08.14 PM.pngThis the season! Around this time last year, I made 3 predictions regarding the 3As: Augmented Reality, Analytics Assistants and Artificial Customers. https://analyticdashboards.wordpress.com/2017/12/22/the-future-of-market-research/ Let’s see what happened and whether you can trust my new predictions for 2019-2020.


AUGMENTED REALITY: I predicted virtual reality losing out to augmented reality, as attested by many useful AR applications, including Gatwick Airport navigation getting the Mobile App of the Year award (https://www.vrfocus.com/2018/05/gatwick-airportsaugmented-reality-passenger-app-wins-awards/), trying on Rolex watches (https://www.vrfocus.com/2018/05/gatwick-airportsaugmented-reality-passenger-app-wins-awards/), Accuvein visualizing the patient’s vein network (https://www.accuvein.com/)  and my above pic with a dinosaur. Most consumers and managers want to to integrate their offline and online identities. In the third annual immersive experiences study, the majority (75%) of the 650 industry professionals believe augmented reality will be bigger than virtual reality in the long term. Many respondents cited augmented reality’s accessibility, being available via smartphones, a broader variety of use cases and integration with the physical world (https://www.mediapost.com/publications/article/323043/augmented-reality-seen-as-beating-out-virtual-real.html)

ANALYTIC ASSISTANTS: A year ago I predicted that analytic dashboards (notsizedata.com) would evolve into ‘always on’ analytic assistants who learn what the decision maker needs them to show: “Just as human market researchers had to evolve to consultants (giving specific and accountable recommendations instead of only collecting and presenting the best data), analytics assistants will evolve to better understand exactly what the decision maker needs and inspire her to action” In September, Adobe introduced an AI-fueled virtual assistant called Intelligent Alerts today to help users find deeper insights they might have otherwise missed. https://techcrunch.com/2018/09/24/adobe-introduces-ai-assistant-to-help-analytics-users-find-deeper-insights/). In the words of John Bates: “Historically we’ve analyzed the data that we collect on behalf of our customers, on behalf of brands and help provide insights. Now we’re analyzing our users’ behavior within Adobe Analytics, and then mashing them up with those insights that are most relevant and personalized for that individual, based on the signals that we see and how they use our tool”. Exactly.

Screen Shot 2018-12-27 at 10.05.36 AM.pngARTIFICIAL CUSTOMERS: As I predicted, customers increasingly rely on recommendation systems, Amazon is employing the flywheel approach to leverage machine learning from one part of the organization to others (https://www.forbes.com/sites/blakemorgan/2018/07/16/how-amazon-has-re-organized-around-artificial-intelligence-and-machine-learning/#4ffdc3ea7361) and Artificial Intelligence has taught itself to think smarter, knowing what to ponder and what to ignore (http://science.sciencemag.org/content/362/6419/1140.full). At the same time, sellers are being replaced by AI: ISMS-MSI practice prize finalist Yael Karlinsky Shichor shows how automating the B2B Salesperson can improve Pricing Decisions for Hadco Metal (http://lilienpracticeprizevideos.org/hadco-metal/#more-244). It is only a matter of time before B2B buyers will also automate their purchase negotiations, leading to a Battle of the Bots. While marketing and market research has focused forever on understanding and nudging human behavior, we now have to understand how machines interact in the marketplace.


So here are some new predictions for the next two years and beyond:


From polyamorous consumers to lock-in subscription platforms

Consumers are typically not fully loyal to a given brand; instead they choose among the considered brands based on e.g. occasion and price promotions. Web 1.0 did not diminish the power of brands, as predicted by the ‘frictionless commerce’ concept, while Web 2.0 made it easier for consumers to discover new alternatives through reviews and social media. Nowadays however, many consumers subscribe to subscription services, either category-specific (e.g. the Dollar Shave Club) or across categories (e.g. Amazon Prime). For most daily needs, they prefer the convenience, efficiency and predictably good experience over the typical ‘journey’ to choose the one best alternative for the occasion. Such platform create lock in both in minds (loyalty points and free shipping) and in hearts (loving the values and satisfaction of all my basic needs while freeing up my time for fulfilling experiences). This cognitive and lock-in enables winner-takes-all segments, with consumer choice mostly between competing platforms instead of between competing product brands. Each platform knows more than ever about its own customers – witness the conversational AI advancements from 162K hours of consumer conversations with Alexa (https://blog.aboutamazon.com/amazon-ai/alexa-lets-chat). However, learning about the competing platforms’ consumers is tough, and the holy grail for a new era of competitive intelligence.

Screen Shot 2018-12-27 at 10.07.16 AM

Innovate at the ends: From market research analysis to Asking & Advising Skills

Traditional market research providers prided themselves on the best data and analysis, leaving the research questions and insights to their clients. In this century, the ‘perfect’ data and analysis are no longer valued by clients – fast ‘good enough’ answers to the right questions are king. However, decision makers are waking up to the limits of the ‘fast food’ data that online giants such as Facebook and YouTube provide, as these free online metrics do not align with their brand objectives (http://www.msi.org/reports/do-online-behavior-tracking-or-attitude-survey-metrics-drive-brand-sales-an/). They crave human assistance in helping them formulate the right questions to ask, and translate analysis into actionable insights and advice. While the middle steps of market research in the below figure are mostly automated, clients need a trusted advisor at the start (why is your current issue important? What is the real problem?) and at the end (knowing you and your organization, how can we implement the insights?). My experience mirrors the ’10 sources of innovation’ observation (https://www.doblin.com/ten-types) that 90% of top 500 firm innovations focus on the middle of the value chain (core and expanded product), while most value is to be uncovered at the start (business model) and the end (customer experience).  That is great news for employees and consultants who excel in those skills. In below figure, I advise to turn the ‘frown’ of traditional market research priorities into a ‘smile’:

Screen Shot 2018-12-27 at 9.57.53 AM


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